Self-optimizing classifier

• US 7,362,892 B2
• Filed: 07/02/2003
• Issued: 04/22/2008
• Est. Priority Date: 07/02/2003
• Status: Expired due to Fees

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Paradis, Rosemary D., Tillotson, Dennis A., Lewis, Lori K.
• Primary Examiner(s): Wu; Jingge
• Assistant Examiner(s): Seth; Manav
• Application Number:
  US10/612,808
• Publication Number:
  US 20050100209A1
• Time in Patent Office: 1,756 Days
• Field of Search: 382155-161, 382/181, 382224-228, 707 3- 6
• US Class Current: 382/159
• CPC Class Codes: G06F 18/21 Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

A method and computer program product are disclosed for determining an optimal classifier model for a pattern recognition system and updating the determined model to recognize new output classes. An initial plurality of classifier models are selected from a set of generated classifier models. An optimal representative classifier for each classifier model is selected according to an iterative optimization routine. The representative classifier having the highest associated value for a fitness function is accepted.

Claims

1. A method for determining an optimal classifier model for a pattern recognition system, comprising:

selecting a plurality of initial classifier models from a set of available classifier models;

producing an optimal representative classifier for each classifier model according to an iterative optimization routine, the iterative optimization routine comprising iterative repetition of the following steps until the occurrence of a termination event;

determining an architecture for each selected classifier model according to an optimization algorithm;

optimizing at least one numerical parameter within the determined architecture; and

determining a set of optimum features for the determined architecture; and

accepting the representative classifier having a best associated value for a fitness function.

2. A method as set forth in claim 1, wherein the step of selecting a set of features includes the use of principle component analysis.

3. A method as set forth in claim 1, wherein the architecture is determined according to a genetic optimization algorithm.

4. A method as set forth in claim 1, wherein the classifier model is automatically updated to recognize additional classes, further comprising the following steps:

grouping a plurality of rejected pattern samples into clusters according to the similarities between the pattern samples;

searching at least one database to identify at least one of the clusters of rejected pattern samples;

submitting at least one cluster not identified in the database search to an expert verifier for identification if one or more clusters are not identified in the database search; and

adding the pattern samples from the identified clusters to a set of training samples.

5. A method as set forth in claim 1, wherein the method further comprises the step of determining a set of output classes via a clustering algorithm and associating each of a plurality of training samples with an output class.

6. A computer program product comprising a computer readable medium encoded with computer executable instructions, operative in a data processing system, for determining an optimal classifier model for a pattern recognition system, comprising:

a system control program that selects a plurality of classifier models from a set of available classifier models; and

an iterative loop that produces an optimal representative classifier for each classifier model according to an iterative optimization routine, the iterative loop comprising;

an architecture determination portion that determines an architecture for each selected classifier model according to an optimization algorithm;

a parameter optimizer that optimizes at least one parameter within the determined architecture; and

a feature optimizer that selects an optimal set of features for the determined architecture;

wherein the system control program accepts the representative classifier having a best associated value for a fitness function upon termination of the iterative optimization routine.

7. A method for determining an optimal classifier model for a pattern recognition system, comprising:

selecting a plurality of initial classifier models from a set of available classifier models;

producing an optimal representative classifier for each classifier model according to an iterative optimization routine;

accepting the representative classifier having a best associated value for a fitness function; and

updating the accepted classifier to recognize additional classes, wherein updating the accepted classifier comprises;

grouping a plurality of rejected pattern samples into clusters according to the similarities between the pattern samples;

searching at least one database to identify at least one of the clusters of rejected pattern samples;

submitting at least one cluster not identified in the database search to an expert verifier for identification if one or more clusters are not identified in the database search; and

adding the pattern samples from the identified clusters to a set of training samples.

8. A method as set forth in claim 7, wherein the step of producing an optimal representative of a classifier model includes iteratively repeating the following steps until the occurrence of a termination event:

classifier model includes iteratively repeating the following steps until the occurrence of a termination event;

determining an architecture for each selected classifier model according to an optimization algorithm;

optimizing at least one numerical parameter within the determined architecture; and

determining a set of optimum features for the determined architecture.

9. A method as set forth in claim 8, wherein the step of selecting a set of features includes the use of principle component analysis.

10. A method as set forth in claim 8, wherein the architecture is determined according to a genetic optimization algorithm.

11. A method as set forth in claim 7, wherein the method further comprises the step of determining a set of output classes via a clustering algorithm and associating each of a plurality of training samples with an output class.